Identity theft and on-line fraud have become widespread problems in the United States. Each year, many adults in the U.S. have their identities stolen and numerous accounts are compromised, leading to significant losses as a result of identity theft. Weak authentication has led to Internet identity theft, phishing, and on-line financial fraud. As more consumers use computers and mobile devices for shopping, managing their finances, and accessing health care information, the risk of fraud and identity theft increases. Because of the impact of identity theft and on-line fraud on on-line businesses, more and more enterprises are evaluating authentication and security options for their on-line consumer base. The Federal Financial Institutions Examination Council (FFIEC) Guidance of 2005, which was updated in 2011, requires online financial institutions to apply strong authentication. Many fraud detection systems use a rules engine and a behavioral engine to assess the level of risk associated with an online transaction. Typical fraud detection systems analyze transactions at a user-level in that a behavioral engine learns how a consumer uses the system to dynamically identify risk. The fraud detection system responds when consumer behavior changes, even if the change does not break a general rule. For example, a behavioral engine goes on alert when a consumer who always logs on from home suddenly logs in from another country.
On average, 5% of a consumer's activity is unusual, but legitimate behavior, resulting in a high volume of transactions being falsely flagged as suspicious transactions in fraud detection systems. The level of the false positive rate is a large concern to enterprises using such fraud detection systems because a high level of false alerts can lead to enterprises unnecessarily investigating or intervening in a large number of transactions. Unwarranted investigations and interventions are costly for an enterprise. In addition, a high level of false alerts can detract from the true risky transactions. Traditionally, fraud detection systems have reduced the number of false positives by raising the risk threshold, such that transactions with lower risk scores would not be intervened or investigated. Such conventional solutions, however, miss the detection of real fraud transactions.